Overview

Dataset statistics

Number of variables10
Number of observations72
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory49.8 B

Variable types

TimeSeries10

Alerts

Active Truck Utilization (SA) is highly overall correlated with Total TL: Spot Rate (exc. FSC, SA) and 1 other fieldsHigh correlation
Total Truck Loadings (SA) is highly overall correlated with Total TL: Spot Rate (exc. FSC, SA) and 5 other fieldsHigh correlation
Total TL: Spot Rate (exc. FSC, SA) is highly overall correlated with Active Truck Utilization (SA) and 4 other fieldsHigh correlation
Total TL: Contract Rate (exc. FSC, SA) is highly overall correlated with Total Truck Loadings (SA) and 5 other fieldsHigh correlation
Driver Labor Index (1992=100, SA) is highly overall correlated with Total Truck Loadings (SA) and 4 other fieldsHigh correlation
Truck Driver Pressure Index (0 = Neutral, SA) is highly overall correlated with Active Truck Utilization (SA)High correlation
Real GDP is highly overall correlated with Total Truck Loadings (SA) and 5 other fieldsHigh correlation
CPI Index is highly overall correlated with Total Truck Loadings (SA) and 5 other fieldsHigh correlation
3 Month T-Bill Rate, % is highly overall correlated with Total Truck Loadings (SA) and 4 other fieldsHigh correlation
Active Truck Utilization (SA) is non stationaryNon stationary
Total Truck Loadings (SA) is non stationaryNon stationary
Total TL: Spot Rate (exc. FSC, SA) is non stationaryNon stationary
Total TL: Contract Rate (exc. FSC, SA) is non stationaryNon stationary
Real GDP is non stationaryNon stationary
CPI Index is non stationaryNon stationary
3 Month T-Bill Rate, % is non stationaryNon stationary
National Avg. Diesel Fuel Price ($/Gal.) is non stationaryNon stationary
Active Truck Utilization (SA) is seasonalSeasonal
Total Truck Loadings (SA) has unique valuesUnique
Total TL: Spot Rate (exc. FSC, SA) has unique valuesUnique
Total TL: Contract Rate (exc. FSC, SA) has unique valuesUnique
Driver Labor Index (1992=100, SA) has unique valuesUnique
Truck Driver Pressure Index (0 = Neutral, SA) has unique valuesUnique
Real GDP has unique valuesUnique
CPI Index has unique valuesUnique
National Avg. Diesel Fuel Price ($/Gal.) has unique valuesUnique

Reproduction

Analysis started2023-05-02 05:05:03.524525
Analysis finished2023-05-02 05:05:18.530946
Duration15.01 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Active Truck Utilization (SA)
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct67
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90656425
Minimum0.81875342
Maximum1
Zeros0
Zeros (%)0.0%
Memory size416.0 B
2023-05-01T22:05:18.619812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.81875342
5-th percentile0.84551112
Q10.87281978
median0.89221036
Q30.93374874
95-th percentile1
Maximum1
Range0.18124658
Interquartile range (IQR)0.060928956

Descriptive statistics

Standard deviation0.049937829
Coefficient of variation (CV)0.055084711
Kurtosis-0.64183795
Mean0.90656425
Median Absolute Deviation (MAD)0.027965426
Skewness0.63223249
Sum65.272626
Variance0.0024937866
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0004575431117
2023-05-01T22:05:18.826426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6
 
8.3%
0.8802774549 1
 
1.4%
0.9932938218 1
 
1.4%
0.9874635339 1
 
1.4%
0.9832124114 1
 
1.4%
0.952093184 1
 
1.4%
0.9147752523 1
 
1.4%
0.8938692808 1
 
1.4%
0.8767299652 1
 
1.4%
0.8730381131 1
 
1.4%
Other values (57) 57
79.2%
ValueCountFrequency (%)
0.8187534213 1
1.4%
0.828115344 1
1.4%
0.8424383402 1
1.4%
0.8449956775 1
1.4%
0.8459328413 1
1.4%
0.849678874 1
1.4%
0.8502241373 1
1.4%
0.8508908153 1
1.4%
0.8517776132 1
1.4%
0.8550214767 1
1.4%
ValueCountFrequency (%)
1 6
8.3%
0.9972336292 1
 
1.4%
0.9968987107 1
 
1.4%
0.9932938218 1
 
1.4%
0.9874635339 1
 
1.4%
0.9832124114 1
 
1.4%
0.9775281549 1
 
1.4%
0.9677611589 1
 
1.4%
0.9565317631 1
 
1.4%
0.9528998733 1
 
1.4%
2023-05-01T22:05:18.957843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Total Truck Loadings (SA)
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7571615 × 108
Minimum1.4217122 × 108
Maximum1.9923736 × 108
Zeros0
Zeros (%)0.0%
Memory size704.0 B
2023-05-01T22:05:19.256811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.4217122 × 108
5-th percentile1.4803801 × 108
Q11.5842818 × 108
median1.7868644 × 108
Q31.9254712 × 108
95-th percentile1.9764387 × 108
Maximum1.9923736 × 108
Range57066141
Interquartile range (IQR)34118932

Descriptive statistics

Standard deviation18219284
Coefficient of variation (CV)0.10368588
Kurtosis-1.5504415
Mean1.7571615 × 108
Median Absolute Deviation (MAD)15469235
Skewness-0.23604495
Sum1.2651563 × 1010
Variance3.3194233 × 1014
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9073444762
2023-05-01T22:05:19.409919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171623983.4 1
 
1.4%
168837697.9 1
 
1.4%
190820496.9 1
 
1.4%
188672974.2 1
 
1.4%
185505302.7 1
 
1.4%
171777406 1
 
1.4%
190536392.5 1
 
1.4%
191394266.8 1
 
1.4%
191989957 1
 
1.4%
191406348.9 1
 
1.4%
Other values (62) 62
86.1%
ValueCountFrequency (%)
142171223.1 1
1.4%
144844283.9 1
1.4%
145609965 1
1.4%
145958760.8 1
1.4%
149739215.2 1
1.4%
150792516.4 1
1.4%
152612313.4 1
1.4%
152832222.1 1
1.4%
152865448.6 1
1.4%
153261879 1
1.4%
ValueCountFrequency (%)
199237364.1 1
1.4%
199089774.9 1
1.4%
198890804.9 1
1.4%
198000980.3 1
1.4%
197351692.2 1
1.4%
196971718.7 1
1.4%
196335490.8 1
1.4%
196133989.9 1
1.4%
195511981.7 1
1.4%
194944432.8 1
1.4%
2023-05-01T22:05:19.544594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Total TL: Spot Rate (exc. FSC, SA)
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.67607
Minimum77.902023
Maximum162.4313
Zeros0
Zeros (%)0.0%
Memory size416.0 B
2023-05-01T22:05:19.881256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum77.902023
5-th percentile85.267386
Q1103.12238
median108.9407
Q3119.991
95-th percentile149.67843
Maximum162.4313
Range84.529282
Interquartile range (IQR)16.868616

Descriptive statistics

Standard deviation17.677216
Coefficient of variation (CV)0.15688526
Kurtosis0.89831018
Mean112.67607
Median Absolute Deviation (MAD)7.5061531
Skewness0.80815655
Sum8112.6774
Variance312.48395
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3826696339
2023-05-01T22:05:20.030257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1
 
1.4%
114.7693176 1
 
1.4%
145.5853577 1
 
1.4%
142.4832306 1
 
1.4%
129.2870789 1
 
1.4%
96.45489502 1
 
1.4%
109.3403091 1
 
1.4%
105.4861298 1
 
1.4%
104.580986 1
 
1.4%
101.0910568 1
 
1.4%
Other values (62) 62
86.1%
ValueCountFrequency (%)
77.90202332 1
1.4%
80.17908478 1
1.4%
81.82704163 1
1.4%
82.68315125 1
1.4%
87.38175964 1
1.4%
93.94298553 1
1.4%
95.59709167 1
1.4%
95.89161682 1
1.4%
96.11296844 1
1.4%
96.45489502 1
1.4%
ValueCountFrequency (%)
162.4313049 1
1.4%
157.1585999 1
1.4%
157.1009979 1
1.4%
154.681076 1
1.4%
145.5853577 1
1.4%
142.4832306 1
1.4%
135.1296234 1
1.4%
135.0182037 1
1.4%
134.4703217 1
1.4%
131.2262726 1
1.4%
2023-05-01T22:05:20.155379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Total TL: Contract Rate (exc. FSC, SA)
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.85154
Minimum95.654587
Maximum156.7821
Zeros0
Zeros (%)0.0%
Memory size416.0 B
2023-05-01T22:05:20.432310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum95.654587
5-th percentile96.518226
Q1105.69841
median114.89983
Q3139.07974
95-th percentile152.066
Maximum156.7821
Range61.127518
Interquartile range (IQR)33.381336

Descriptive statistics

Standard deviation18.20997
Coefficient of variation (CV)0.1506805
Kurtosis-1.0775963
Mean120.85154
Median Absolute Deviation (MAD)12.947575
Skewness0.431725
Sum8701.3107
Variance331.60306
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.976873648
2023-05-01T22:05:20.581294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1
 
1.4%
101.4797134 1
 
1.4%
135.536911 1
 
1.4%
132.0414886 1
 
1.4%
126.2863235 1
 
1.4%
120.8508377 1
 
1.4%
124.3693542 1
 
1.4%
125.0056305 1
 
1.4%
125.241539 1
 
1.4%
126.0108719 1
 
1.4%
Other values (62) 62
86.1%
ValueCountFrequency (%)
95.65458679 1
1.4%
96.18639374 1
1.4%
96.26087189 1
1.4%
96.37526703 1
1.4%
96.63519287 1
1.4%
97.5608902 1
1.4%
98.60038757 1
1.4%
100 1
1.4%
100.1637192 1
1.4%
100.5591888 1
1.4%
ValueCountFrequency (%)
156.7821045 1
1.4%
155.7073364 1
1.4%
154.4971771 1
1.4%
152.3362732 1
1.4%
151.8448639 1
1.4%
150.3144073 1
1.4%
149.0257721 1
1.4%
147.7256775 1
1.4%
146.0992279 1
1.4%
145.7658234 1
1.4%
2023-05-01T22:05:20.707431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Driver Labor Index (1992=100, SA)
Numeric time series

HIGH CORRELATION  UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.4314
Minimum116.56871
Maximum130.13795
Zeros0
Zeros (%)0.0%
Memory size416.0 B
2023-05-01T22:05:20.987580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum116.56871
5-th percentile121.12291
Q1124.48163
median125.20852
Q3126.53544
95-th percentile129.78412
Maximum130.13795
Range13.569244
Interquartile range (IQR)2.0538044

Descriptive statistics

Standard deviation2.5168931
Coefficient of variation (CV)0.020065894
Kurtosis2.9280243
Mean125.4314
Median Absolute Deviation (MAD)0.89712906
Skewness-0.83188593
Sum9031.0606
Variance6.3347507
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.005218929257
2023-05-01T22:05:21.145954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116.5687103 1
 
1.4%
117.6085205 1
 
1.4%
124.3835754 1
 
1.4%
125.1694717 1
 
1.4%
125.2924347 1
 
1.4%
125.0290222 1
 
1.4%
126.7693634 1
 
1.4%
127.6294556 1
 
1.4%
126.8488922 1
 
1.4%
125.9845963 1
 
1.4%
Other values (62) 62
86.1%
ValueCountFrequency (%)
116.5687103 1
1.4%
117.6085205 1
1.4%
118.8349915 1
1.4%
120.3287048 1
1.4%
121.7727203 1
1.4%
123.6926575 1
1.4%
123.7494354 1
1.4%
123.9109192 1
1.4%
123.9218445 1
1.4%
123.9808655 1
1.4%
ValueCountFrequency (%)
130.1379547 1
1.4%
130.0317841 1
1.4%
129.9294891 1
1.4%
129.8338623 1
1.4%
129.7434235 1
1.4%
129.6118164 1
1.4%
129.4287872 1
1.4%
129.1701508 1
1.4%
128.8356934 1
1.4%
128.3970947 1
1.4%
2023-05-01T22:05:21.282438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Truck Driver Pressure Index (0 = Neutral, SA)
Numeric time series

HIGH CORRELATION  UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.77518367
Minimum-11.97446
Maximum11.560026
Zeros0
Zeros (%)0.0%
Memory size416.0 B
2023-05-01T22:05:21.563689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-11.97446
5-th percentile-10.150674
Q1-4.8870673
median-1.9088657
Q33.6330746
95-th percentile10.199919
Maximum11.560026
Range23.534486
Interquartile range (IQR)8.520142

Descriptive statistics

Standard deviation6.2282362
Coefficient of variation (CV)-8.0345296
Kurtosis-0.65586764
Mean-0.77518367
Median Absolute Deviation (MAD)3.7405548
Skewness0.37698188
Sum-55.813225
Variance38.790928
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0001443657823
2023-05-01T22:05:21.704771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.301321268 1
 
1.4%
-2.248936415 1
 
1.4%
9.411432266 1
 
1.4%
8.962598801 1
 
1.4%
2.042728186 1
 
1.4%
-5.647425652 1
 
1.4%
-4.393026829 1
 
1.4%
-4.725173473 1
 
1.4%
-5.536151886 1
 
1.4%
-4.978411198 1
 
1.4%
Other values (62) 62
86.1%
ValueCountFrequency (%)
-11.97445965 1
1.4%
-11.75789738 1
1.4%
-11.24374199 1
1.4%
-10.8213501 1
1.4%
-9.601939201 1
1.4%
-9.476758003 1
1.4%
-9.013209343 1
1.4%
-8.931984901 1
1.4%
-7.21913147 1
1.4%
-7.036275864 1
1.4%
ValueCountFrequency (%)
11.56002617 1
1.4%
11.26981544 1
1.4%
10.71324921 1
1.4%
10.46580029 1
1.4%
9.982379913 1
1.4%
9.524805069 1
1.4%
9.411432266 1
1.4%
8.962598801 1
1.4%
8.783802032 1
1.4%
8.387318611 1
1.4%
2023-05-01T22:05:21.826051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Real GDP
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17929.577
Minimum15161.772
Maximum21040.896
Zeros0
Zeros (%)0.0%
Memory size704.0 B
2023-05-01T22:05:22.104965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum15161.772
5-th percentile15373.508
Q116275.045
median17768.524
Q319576.334
95-th percentile20668.24
Maximum21040.896
Range5879.1245
Interquartile range (IQR)3301.2897

Descriptive statistics

Standard deviation1817.1487
Coefficient of variation (CV)0.10134923
Kurtosis-1.3280313
Mean17929.577
Median Absolute Deviation (MAD)1551.6775
Skewness0.11028557
Sum1290929.5
Variance3302029.6
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9741050644
2023-05-01T22:05:22.256066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15702.906 1
 
1.4%
15792.773 1
 
1.4%
19216.22461 1
 
1.4%
18924.26172 1
 
1.4%
18743.7207 1
 
1.4%
17378.71289 1
 
1.4%
18989.87695 1
 
1.4%
19215.69141 1
 
1.4%
19130.93164 1
 
1.4%
18962.17578 1
 
1.4%
Other values (62) 62
86.1%
ValueCountFrequency (%)
15161.772 1
1.4%
15187.475 1
1.4%
15216.647 1
1.4%
15366.607 1
1.4%
15379.155 1
1.4%
15456.059 1
1.4%
15605.628 1
1.4%
15702.906 1
1.4%
15709.562 1
1.4%
15726.282 1
1.4%
ValueCountFrequency (%)
21040.89648 1
1.4%
20933.94531 1
1.4%
20828.23828 1
1.4%
20724.76172 1
1.4%
20621.99414 1
1.4%
20525.94141 1
1.4%
20436.61523 1
1.4%
20356.19141 1
1.4%
20294.55273 1
1.4%
20280.16992 1
1.4%
2023-05-01T22:05:22.385199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

CPI Index
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.546312
Minimum2.1237767
Maximum3.3580749
Zeros0
Zeros (%)0.0%
Memory size416.0 B
2023-05-01T22:05:22.668993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.1237767
5-th percentile2.146711
Q12.2958274
median2.4298667
Q32.6986105
95-th percentile3.2729686
Maximum3.3580749
Range1.2342982
Interquartile range (IQR)0.40278304

Descriptive statistics

Standard deviation0.35546234
Coefficient of variation (CV)0.1395989
Kurtosis-0.24397413
Mean2.546312
Median Absolute Deviation (MAD)0.16702521
Skewness0.94295281
Sum183.33446
Variance0.12635347
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9990627121
2023-05-01T22:05:22.823601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.127696753 1
 
1.4%
2.155376673 1
 
1.4%
2.635246992 1
 
1.4%
2.608789921 1
 
1.4%
2.594377041 1
 
1.4%
2.564182997 1
 
1.4%
2.586179972 1
 
1.4%
2.577852964 1
 
1.4%
2.562249899 1
 
1.4%
2.552829981 1
 
1.4%
Other values (62) 62
86.1%
ValueCountFrequency (%)
2.123776674 1
1.4%
2.127696753 1
1.4%
2.135070086 1
1.4%
2.138486624 1
1.4%
2.153439999 1
1.4%
2.155376673 1
1.4%
2.170300007 1
1.4%
2.172973394 1
1.4%
2.17373991 1
1.4%
2.179343224 1
1.4%
ValueCountFrequency (%)
3.358074903 1
1.4%
3.335891962 1
1.4%
3.312577963 1
1.4%
3.287779093 1
1.4%
3.260850906 1
1.4%
3.23054409 1
1.4%
3.196275949 1
1.4%
3.159301043 1
1.4%
3.120743036 1
1.4%
3.082120895 1
1.4%
2023-05-01T22:05:22.954091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

3 Month T-Bill Rate, %
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct63
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.324479
Minimum0.013333334
Maximum5.4191208
Zeros0
Zeros (%)0.0%
Memory size416.0 B
2023-05-01T22:05:23.236409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.013333334
5-th percentile0.025166667
Q10.065833336
median0.27499999
Q32.0800003
95-th percentile5.2472093
Maximum5.4191208
Range5.4057875
Interquartile range (IQR)2.0141669

Descriptive statistics

Standard deviation1.7536676
Coefficient of variation (CV)1.3240433
Kurtosis0.23175991
Mean1.324479
Median Absolute Deviation (MAD)0.24833331
Skewness1.2610673
Sum95.362489
Variance3.07535
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.2329025487
2023-05-01T22:05:23.382212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08666666597 3
 
4.2%
0.04666666687 2
 
2.8%
0.03333333507 2
 
2.8%
0.05000000075 2
 
2.8%
0.1566666663 2
 
2.8%
0.02333333343 2
 
2.8%
0.02666666731 2
 
2.8%
0.1266666651 2
 
2.8%
2.089999914 1
 
1.4%
1.096667051 1
 
1.4%
Other values (53) 53
73.6%
ValueCountFrequency (%)
0.01333333366 1
1.4%
0.01999999955 1
1.4%
0.02333333343 2
2.8%
0.02666666731 2
2.8%
0.02666700073 1
1.4%
0.03333333507 2
2.8%
0.03999999911 1
1.4%
0.04666666687 2
2.8%
0.04666699842 1
1.4%
0.05000000075 2
2.8%
ValueCountFrequency (%)
5.419120789 1
1.4%
5.414663792 1
1.4%
5.394785881 1
1.4%
5.321630955 1
1.4%
5.186318874 1
1.4%
5.01742506 1
1.4%
4.701289177 1
1.4%
4.62273407 1
1.4%
4.266119957 1
1.4%
4.18333292 1
1.4%
2023-05-01T22:05:23.506942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

National Avg. Diesel Fuel Price ($/Gal.)
Numeric time series

NON STATIONARY  UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4358049
Minimum2.0769999
Maximum5.4816666
Zeros0
Zeros (%)0.0%
Memory size416.0 B
2023-05-01T22:05:23.788446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.0769999
5-th percentile2.3572334
Q12.8650834
median3.4398334
Q33.9819166
95-th percentile4.4520815
Maximum5.4816666
Range3.4046667
Interquartile range (IQR)1.1168332

Descriptive statistics

Standard deviation0.75978369
Coefficient of variation (CV)0.22113703
Kurtosis-0.43823621
Mean3.4358049
Median Absolute Deviation (MAD)0.57199991
Skewness0.26367587
Sum247.37795
Variance0.57727122
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.1684137527
2023-05-01T22:05:23.942355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.522000074 1
 
1.4%
4.39533329 1
 
1.4%
2.893333435 1
 
1.4%
2.468666553 1
 
1.4%
2.425666571 1
 
1.4%
2.430999994 1
 
1.4%
2.895666599 1
 
1.4%
3.059000015 1
 
1.4%
3.022000074 1
 
1.4%
3.123666763 1
 
1.4%
Other values (62) 62
86.1%
ValueCountFrequency (%)
2.076999903 1
1.4%
2.193000078 1
1.4%
2.296666622 1
1.4%
2.325333357 1
1.4%
2.383333445 1
1.4%
2.425666571 1
1.4%
2.430999994 1
1.4%
2.431999922 1
1.4%
2.467666626 1
1.4%
2.468666553 1
1.4%
ValueCountFrequency (%)
5.481666565 1
1.4%
5.164000034 1
1.4%
5.059999943 1
1.4%
4.521440506 1
1.4%
4.39533329 1
1.4%
4.374940395 1
1.4%
4.342999935 1
1.4%
4.287000179 1
1.4%
4.121421814 1
1.4%
4.096830368 1
1.4%
2023-05-01T22:05:24.074946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Interactions

2023-05-01T22:05:16.726919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:03.874180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:05.286544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:06.783880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:08.217563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:09.765170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:11.201685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:12.640668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:14.038018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:15.400813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:16.873546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:04.052071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:05.425729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:06.950671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:08.381836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:09.923662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:11.349632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:12.788701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:14.187568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:15.540485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:17.017298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:04.192494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:05.567303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:07.099797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:08.542627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:10.074193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:11.491654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:12.937521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:14.326402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:15.674596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:17.155306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:04.310313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:05.702463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:07.237829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:08.676997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:10.211971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:11.645401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:13.073900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:14.451606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:15.798663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:17.291138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:04.441136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:05.842471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:07.371513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:08.822708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:10.345303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:11.781653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:13.217515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:14.580394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:15.926098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:17.437557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:04.579161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:05.997634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:07.524001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:08.993169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:10.490457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:11.918027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:13.361093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:14.722022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:16.062329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:17.568855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:04.710791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:06.136012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:07.658878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:09.165821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:10.620146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:12.046553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:13.495968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:14.847437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:16.183108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:17.711222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:04.849778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:06.282538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:07.794740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:09.332113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:10.759528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:12.200627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:13.633375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:14.985507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:16.326451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:17.851657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:04.989488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:06.424520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:07.934593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:09.477392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:10.907564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:12.353343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:13.768023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:15.119300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:16.454445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:17.981235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:05.130713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:06.557760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:08.069504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:09.621439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:11.047240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:12.494151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:13.899483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:15.259275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-01T22:05:16.581200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-01T22:05:24.344139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Active Truck Utilization (SA)Total Truck Loadings (SA)Total TL: Spot Rate (exc. FSC, SA)Total TL: Contract Rate (exc. FSC, SA)Driver Labor Index (1992=100, SA)Truck Driver Pressure Index (0 = Neutral, SA)Real GDPCPI Index3 Month T-Bill Rate, %National Avg. Diesel Fuel Price ($/Gal.)
Active Truck Utilization (SA)1.0000.2090.5050.2050.1090.9300.1460.137-0.1320.030
Total Truck Loadings (SA)0.2091.0000.6770.9510.728-0.0560.9510.9330.6500.378
Total TL: Spot Rate (exc. FSC, SA)0.5050.6771.0000.7190.3650.3080.6460.6370.1410.410
Total TL: Contract Rate (exc. FSC, SA)0.2050.9510.7191.0000.723-0.0440.9660.9660.5070.359
Driver Labor Index (1992=100, SA)0.1090.7280.3650.7231.000-0.0750.7940.7900.6890.414
Truck Driver Pressure Index (0 = Neutral, SA)0.930-0.0560.308-0.044-0.0751.000-0.093-0.097-0.339-0.133
Real GDP0.1460.9510.6460.9660.794-0.0931.0000.9910.5850.340
CPI Index0.1370.9330.6370.9660.790-0.0970.9911.0000.5620.325
3 Month T-Bill Rate, %-0.1320.6500.1410.5070.689-0.3390.5850.5621.0000.293
National Avg. Diesel Fuel Price ($/Gal.)0.0300.3780.4100.3590.414-0.1330.3400.3250.2931.000

Missing values

2023-05-01T22:05:18.188048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-01T22:05:18.426569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

1Active Truck Utilization (SA)Total Truck Loadings (SA)Total TL: Spot Rate (exc. FSC, SA)Total TL: Contract Rate (exc. FSC, SA)Driver Labor Index (1992=100, SA)Truck Driver Pressure Index (0 = Neutral, SA)Real GDPCPI Index3 Month T-Bill Rate, %National Avg. Diesel Fuel Price ($/Gal.)
00.8802771.716240e+08100.000000100.000000116.568710-3.30132115702.9062.1276972.0900003.522000
10.8923351.688377e+08114.769318101.479713117.608521-2.24893615792.7732.1553771.6533334.395333
20.8837761.632016e+08123.794464102.424789118.834991-3.45421415709.5622.1886101.5200004.343000
30.8815741.610687e+08103.305672100.938591120.328705-4.85661915366.6072.1384870.3033332.967000
40.8572271.497392e+0881.82704296.635193121.772720-6.21063415187.4752.1237770.2166672.193000
50.8496791.459588e+0877.90202396.186394123.749435-6.96874115161.7722.1350700.1733332.325333
60.8508911.448443e+0880.17908596.375267124.274933-5.48257515216.6472.1534400.1566672.600000
70.8502241.421712e+0882.68315196.260872124.340332-4.48375215379.1552.1703000.0566672.736333
80.8712761.456100e+0887.38176095.654587124.995499-1.67666115456.0592.1737400.1066672.848333
90.9036851.507925e+0896.11296897.560890125.3868032.32554915605.6282.1729730.1466673.025333
1Active Truck Utilization (SA)Total Truck Loadings (SA)Total TL: Spot Rate (exc. FSC, SA)Total TL: Contract Rate (exc. FSC, SA)Driver Labor Index (1992=100, SA)Truck Driver Pressure Index (0 = Neutral, SA)Real GDPCPI Index3 Month T-Bill Rate, %National Avg. Diesel Fuel Price ($/Gal.)
620.8449961.928742e+08106.492844140.144791128.397095-11.75789720280.1699223.0821215.4146644.121422
630.8424381.925154e+08109.079544139.054153128.835693-11.97446020294.5527343.1207435.4191214.031275
640.8459331.926422e+08111.320610139.156509129.170151-11.24374220356.1914063.1593015.3947863.843582
650.8564891.935778e+08113.312904140.114548129.428787-9.60193920436.6152343.1962765.3216313.916433
660.8721651.949444e+08116.754303141.287262129.611816-7.21913120525.9414063.2305445.0174253.968425
670.8897351.963355e+08123.345222143.374680129.743423-4.74919820621.9941413.2608514.6227344.061817
680.9039091.980010e+08130.674347146.099228129.833862-2.94880120724.7617193.2877794.2661204.063447
690.9137621.988908e+08134.470322149.025772129.929489-1.88524120828.2382813.3125783.8834684.096830
700.9166091.992374e+08135.129623151.844864130.031784-1.82629120933.9453133.3358923.5861284.065533
710.9073501.990898e+08135.018204154.497177130.137955-2.64668321040.8964843.3580753.3421384.064808